The Power of Docker for Data Science

Releasing an Word document table into the land of markdown, a practical overview of sharing your machine learning model with others, and taking local control of checking the builds of your package across computing architectures.

Episode Links

• This week's curator: Colin Fay - [@_ColinFay]](https://twitter.com/_ColinFay) (Twitter)
• Convert a Word table to Markdown
• How Can Someone Else Use My Model?
• How to debug your package in a {rhub} fedora container before sending to CRAN?
• Entire issue available at rweekly.org/2023-W26

Supplement Resources

• {datapasta} RStudio addins and R functions that make copy-pasting vectors and tables to text painless https://milesmcbain.github.io/datapasta
• Matt Kaye's series "The missing semester of your DS education" https://matthewrkaye.com/series.html#the-missing-semester-of-your-ds-education
• Put R in production: Tools and guides to put R models in production https://putrinprod.com
• {checkhelper} A package to help you deal with devtools::check outputs https://thinkr-open.github.io/checkhelper
• Remote Explorer Visual Studio Code extension https://marketplace.visualstudio.com/items?itemName=ms-vscode.remote-explorer
• {crew}: A distributed worker launcher framework for asynchronous and distributed computing https://wlandau.github.io/crew
• Data4Good Explores Visualizing Freshwater Resources on a Global Scale https://appsilon.com/visualizing-fresh-water-resources-data

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